碩士 / 國立中央大學 / 資訊工程學系 / 105 / Self-propelled vehicles have been a popular topic in the past few years. Self-propelled vehicle research aims at reducing human resources and traffic accidents. A small slowly self-propelled car is widely used and has the space to load the goods. Therefore we hope the self-propelled vehicle integrates self-propelled technology and computer vision would be able to automatically detect and follow a specific pedestrian. In this paper, we development of the automatic following guider vehicle that be used in the delivery service business, regional shopping and sightseeing tours, etc. Furthermore, due to the requirement that need high frequently switching guider in the relevant application areas, our system propose a convenient, fast and robustness system for the guider replacement.
The proposed system consists of two parts: pedestrian detection system for finding pedestrian location coordinates and guider identification system for comparing pedestrians and the pre-defined guider. It’s difficult to detect pedestrians in various environments. We have use a more accurate deep learning technique to achieve pedestrian detection. We are able to find variation-adapted features of pedestrians and promote detection rate by using a convolution neural network. The guider identification system uses another convolution neural network to compare the detected pedestrian and the pre-defined guider to identify the unique pedestrian.
In the experiments, we test several videos which are captured from campus streets and building lobby. In the pedestrian detection system, the detection rate can reach up to 94% and has only 4 × 10-7 false positive rate. We train the deep convolutional neural network model for identifying guider. In the case of 2200 images, the recognition accuracy rate reach up to 94%.
| Identifer | oai:union.ndltd.org:TW/105NCU05392097 |
| Date | January 2017 |
| Creators | Ting-Wen Lin, 林亭彣 |
| Contributors | Din-Chang Tseng, 曾定章 |
| Source Sets | National Digital Library of Theses and Dissertations in Taiwan |
| Language | zh-TW |
| Detected Language | English |
| Type | 學位論文 ; thesis |
| Format | 64 |
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